The goal of rando is to provide easier generating of random numbers in a manner that is context aware, and reproducible.
You can install the released version of rando from CRAN with:
install.packages("rando")
You can install the development version of rando from Github with:
install.packages("remotes")
remotes::install_github("MyKo101/rando")
Once installed, to load rando, use
library(rando)
With rando, generating random numbers becomes incredibly easy, as we do
not need to define how many random numbers we need. rando will figure
out how many you need based on where the number generator is being used.
This works for tibble() declarations
df <- tibble(id = 1:10,
x = r_norm())
df
#> # A tibble: 10 x 2
#> id x
#> <int> <dbl>
#> 1 1 -0.365
#> 2 2 0.173
#> 3 3 -0.294
#> 4 4 0.576
#> 5 5 0.875
#> 6 6 0.359
#> 7 7 -0.527
#> 8 8 -0.819
#> 9 9 -0.990
#> 10 10 0.518and inside of dplyr verbs
mutate(df, y = r_unif())
#> # A tibble: 10 x 3
#> id x y
#> <int> <dbl> <dbl>
#> 1 1 -0.365 0.210
#> 2 2 0.173 0.354
#> 3 3 -0.294 0.317
#> 4 4 0.576 0.0695
#> 5 5 0.875 0.125
#> 6 6 0.359 0.169
#> 7 7 -0.527 0.305
#> 8 8 -0.819 0.601
#> 9 9 -0.990 0.483
#> 10 10 0.518 0.300Parameters can also be used to define the number of values to return. If
parameters are longer than 1, rando will try to return the same number
of random values, unless there is a clash between two of the parameters
r_norm(mean = 1:10)
#> [1] 0.4088105 2.2987041 2.2807546 3.9659070 4.5111552 5.4712253 6.5461452
#> [8] 6.3708207 7.7550056 8.7627581
r_norm(mean=1:10,sd=1:2)
#> Error: Inconsistent parameter lengths supplied to r_norm()If you want to manually define the number of random numbers to be
generated, there are two ways to do it. The old fashioned way: providing
the n argument (this must be named)
r_unif(n=20)
#> [1] 0.75427791 0.97153547 0.06031924 0.43098427 0.45223070 0.54105261
#> [7] 0.13882213 0.86252549 0.31421104 0.97247948 0.29288323 0.03809931
#> [13] 0.55187415 0.51237188 0.45841500 0.12699633 0.15236584 0.08755528
#> [19] 0.78088410 0.83223010Or, if we are generating many random numbers, we can set a default n
value to be used globally
set_n(15)
r_norm(mean=3)
#> [1] 4.001347 2.561471 3.474956 2.312623 2.508933 5.044508 2.586922 3.051763
#> [9] 1.205965 3.220328 3.575350 4.599801 2.599194 4.300862 2.722302
r_binom(size=3)
#> [1] 1 2 0 1 3 0 1 2 1 1 3 0 2 2 0The rando functions also check if parameters being supplied are viable
and throws an informative error if not. This is different to the default
stats random number generating functions, which may return a lot of
NaN values with only a vague warning.
rnorm(n=10,sd=-1)
#> Warning in rnorm(n = 10, sd = -1): NAs produced
#> [1] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
r_norm(sd=-1)
#> Error: sd provided to r_norm() must be strictly positiveAll rando functions can also take a .seed argument which does one of
two things:
- If a numeric is supplied, then
randowill set this as the random seed before generating the values - If a TRUE is supplied, then
randowill randomly generate a numeric value to be used.
If .seed is not NULL (the default), then this seed value (supplied
or generated) will be attached to the output, and can be extracted with
pull_seed()
This allows for greater replicability in results.
r_norm(.seed = 42)
#> [1] 1.37095845 -0.56469817 0.36312841 0.63286260 0.40426832 -0.10612452
#> [7] 1.51152200 -0.09465904 2.01842371 -0.06271410 1.30486965 2.28664539
#> [13] -1.38886070 -0.27878877 -0.13332134
#> attr(,"seed")
#> [1] 42
r_norm(.seed = 42)
#> [1] 1.37095845 -0.56469817 0.36312841 0.63286260 0.40426832 -0.10612452
#> [7] 1.51152200 -0.09465904 2.01842371 -0.06271410 1.30486965 2.28664539
#> [13] -1.38886070 -0.27878877 -0.13332134
#> attr(,"seed")
#> [1] 42
x <- r_norm(.seed=TRUE)
x
#> [1] -1.0515017 2.8143380 1.1880200 -1.2010801 -1.1589546 -0.1876997
#> [7] -0.1515049 0.7168907 -0.2086623 -1.0248107 0.7394365 -0.5944315
#> [13] -1.9588881 0.5869532 0.6124257
#> attr(,"seed")
#> [1] 1020465408
r_norm(.seed=pull_seed(x))
#> [1] -1.0515017 2.8143380 1.1880200 -1.2010801 -1.1589546 -0.1876997
#> [7] -0.1515049 0.7168907 -0.2086623 -1.0248107 0.7394365 -0.5944315
#> [13] -1.9588881 0.5869532 0.6124257
#> attr(,"seed")
#> [1] 1020465408In order to make simulations easier, rando provides the blueprint()
function. This function creates a plan for a simulated dataset using
rando functions.
make_tbl <- blueprint(
x = r_norm(),
y = r_norm()
)
make_tbl(n=2)
#> # A tibble: 2 x 2
#> x y
#> <dbl> <dbl>
#> 1 -1.89 1.34
#> 2 -2.28 0.913
make_tbl(n=5)
#> # A tibble: 5 x 2
#> x y
#> <dbl> <dbl>
#> 1 0.316 -0.154
#> 2 1.86 1.46
#> 3 -0.396 -1.42
#> 4 -1.08 0.481
#> 5 1.75 0.323These blueprints can accept additional arguments and will be generated based on these arguments
make_tbl2 <- blueprint(
x = r_norm(mean=x_mu),
y = r_unif(min=y_min,max=y_max)
)
set_n(10000)
make_tbl2(x_mu = 10, y_min = -10, y_max=-5) %>%
summarise(n = n(), mean_x = mean(x), min_y = min(y), max_y = max(y))
#> # A tibble: 1 x 4
#> n mean_x min_y max_y
#> <int> <dbl> <dbl> <dbl>
#> 1 10000 10.0 -10.0 -5.00This then allows for quick generation of simulation data using pmap()
and analysis using map()
make_sim <- blueprint(
x = r_norm(mean = x_mu),
y = r_norm(mean = 2*x+10, sd = 2)
)
tibble(x_mu = r_unif(n = 5, -10, 10)) %>%
pmap(make_sim, n = 100) %>%
map(lm, formula = y ~ x) %>%
map_dfr(broom::tidy)
#> # A tibble: 10 x 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 9.29 1.35 6.89 5.45e-10
#> 2 x 1.92 0.202 9.48 1.60e-15
#> 3 (Intercept) 8.69 0.723 12.0 5.58e-21
#> 4 x 2.38 0.193 12.3 1.32e-21
#> 5 (Intercept) 10.6 0.726 14.6 2.91e-26
#> 6 x 1.82 0.252 7.20 1.22e-10
#> 7 (Intercept) 10.1 0.770 13.1 3.20e-23
#> 8 x 2.06 0.202 10.2 4.72e-17
#> 9 (Intercept) 9.78 0.426 22.9 3.54e-41
#> 10 x 1.68 0.218 7.72 1.02e-11The majority of random number generating functions from the stats
package have been translated into rando functions. Be sure to look
into the documentation for the rando functions you use, as some have
re-parametrised. Functions names for transitioning from stats to
rando generally follow the same naming convention, that is r*()
becomes r_*(), e.g. r_norm() replaces rnorm(). The only exceptions
are r_tdist() and r_fdist() to take over the roles of rt() and
rf(), respectively. rando also includes several new distributions
such as r_bern() and r_letters().
The r_cdf() function is a dynamic random number generator. It can take
any cdf as an argument and produce random numbers with the associated
distribution.
my_fun <- function(x,beta=1){
if_else(x < 0, 0, 1-exp(-beta*x))
}
set_n(1000)
x_data <- r_cdf(my_fun)
hist(x_data,breaks=seq(0,10,0.1))
Any additional
arguments used by the function, can be passed to r_cdf(), and will be
used in determining the number of values to generate (just as in the
other distribution functions above)
r_cdf(my_fun,beta=1:10)
#> [1] 1.59363151 0.01710057 0.51777959 0.10563731 0.15656352 0.04890561
#> [7] 0.05313754 0.10311007 0.01916289 0.09977221Finally, purrr-style functions can be used for r_cdf() to allow for
even briefer function definitions. These have been extended to allow for
the use of additional named arguments to be passed to these <lambda>
functions. Either .x or .t can be used for the random variable.
set_n(20)
r_cdf(~1-exp(-.x),min=0)
#> [1] 1.00280643 0.51202178 3.15050483 0.38757920 0.16273856 1.37652755
#> [7] 0.41813254 1.14622712 1.26543641 0.01011491 0.65036416 1.35177970
#> [13] 1.25859380 0.30105710 1.45331025 0.22260547 1.71133876 0.12983680
#> [19] 0.41169524 0.26691556
r_cdf(~1-exp(-beta*.x),beta=1:10,min=0,n=10)
#> [1] 0.892275572 0.172501802 0.160342455 0.432735682 0.299936533 0.004011393
#> [7] 0.133234262 0.150531530 0.004047155 0.426167250Please note that the rando project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.